SoroushVahidi/scidocs huggingface dataset:
A Survey of Mobile Crowdsensing Techniques: A Critical Component for the Internet of Things
(2016)

Mobile crowdsensing serves as a critical building block for emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy, and storage) and may sacrifice the Quality-of-Service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing redundant data, data size and load can be significantly reduced, thereby reducing resource cost and facilitating the timely delivery of unique, probably critical information and enhancing QoS. This article presents a survey of existing works on mobile crowdsensing strategies with an emphasis on reducing resource cost and achieving high QoS. We start by introducing the motivation for this survey and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss future research directions for mobile crowdsensing for IoT. The survey addresses a broad range of techniques, methods, models, systems, and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in prior works, but also to discuss their applicability toward the IoT and provide guidance on future research directions for mobile crowdsensing.
End-to-end Learning of Latent Dirichlet Allocation by Mirror-Descent Back Propagation
(2015)

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model, which maximizes the posterior probability of the prediction variable given the input document. Different from traditional variational learning or Gibbs sampling approaches, the proposed learning method applies (i) the mirror descent algorithm for exact maximum a posterior inference and (ii) back propagation with stochastic gradient descent for model parameter estimation, leading to scalable learning of the model in an end-to-end discriminative manner. As a byproduct, we also apply this technique to develop a new learning method for the traditional unsupervised LDA model. Experimental results on two real-world regression and classification tasks show that the proposed methods significantly outperform the previous supervised/unsupervised LDA learning methods.
Multi-Objective Optimization of 50 kW/85 kHz IPT System for Public Transport
(2016)

Inductive power transfer (IPT) is an attractive solution for the automated battery charging of public transport electric vehicles (EVs) with low maintenance requirements. This paper presents the design of a 50 kW/85 kHz contactless EV charger, with a focus on the IPT transmitter and receiver coils. The multi-objective magnetics optimization reveals the Pareto tradeoffs and performance limitations in terms of high efficiency, high power density, and low stray field for high-power IPT systems without moving mechanical parts. A full-scale hardware prototype is realized and experimentally investigated. The dc-dc conversion efficiency, including all the power electronics stages, is measured as 95.8% at 50 kW power transfer across an air gap of 160 mm (coil dimensions 410 × 760 × 60 mm3). With 150 mm coil misalignment in the worst case direction, the dc-dc efficiency drops to 92%. The measurements of the magnetic stray field show compliance with ICNIRP 2010 at 800 mm distance from the IPT coil center.
On the Effectiveness of Minimal Context Selection for Robust Question Answering
(2018)

Machine learning models for question-answering (QA), where given a question 1 and a passage, the learner must select some span in the passage as an answer, are 2 known to be brittle. By inserting a single nuisance sentence into the passage, an 3 adversary can fool the model into selecting the wrong span. A promising new 4 approach for QA decomposes the task into two stages: (i) select relevant sentences 5 from the passage; and (ii) select a span among those sentences. Intuitively, if 6 the sentence selector excludes the offending sentence, then the downstream span 7 selector will be robust. While recent work has hinted at the potential robustness 8 of two-stage QA, these methods have never, to our knowledge, been explicitly 9 combined with adversarial training. This paper offers a thorough empirical in10 vestigation of adversarial robustness, demonstrating that although the two-stage 11 approach lags behind single-stage span selection, adversarial training improves its 12 performance significantly, leading to an improvement of over 22 points in F1 score 13 over the adversarially-trained single-stage model. 14
Hyracks: A flexible and extensible foundation for data-intensive computing
(2011)

Hyracks is a new partitioned-parallel software platform designed to run data-intensive computations on large shared-nothing clusters of computers. Hyracks allows users to express a computation as a DAG of data operators and connectors. Operators operate on partitions of input data and produce partitions of output data, while connectors repartition operators' outputs to make the newly produced partitions available at the consuming operators. We describe the Hyracks end user model, for authors of dataflow jobs, and the extension model for users who wish to augment Hyracks' built-in library with new operator and/or connector types. We also describe our initial Hyracks implementation. Since Hyracks is in roughly the same space as the open source Hadoop platform, we compare Hyracks with Hadoop experimentally for several different kinds of use cases. The initial results demonstrate that Hyracks has significant promise as a next-generation platform for data-intensive applications.
An improved traffic signs recognition and tracking method for driver assistance system
(2016)

We introduce a new computer vision based system for robust traffic sign recognition and tracking. Such a system presents a vital support for driver assistance in an intelligent automotive. Firstly, a color based segmentation method is applied to generate traffic sign candidate regions. Secondly, the HoG features are extracted to encode the detected traffic signs and then generating the feature vector. This vector is used as an input to an SVM classifier to identify the traffic sign class. Finally, a tracking method based on optical flow is performed to ensure a continuous capture of the recognized traffic sign while accelerating the execution time. Our method affords high precision rates under different challenging conditions.
Applying a flexible microactuator to robotic mechanisms
(1992)

A flexible microactuator (FMA) driven by an electropneumatic (or electrohydraulic) system has been developed. It has three degrees of freedom-pitch, yaw, and stretch-making it suitable for robotic mechanisms such as fingers, arms, or legs. It is made of fiber-reinforced rubber, and the mechanism is very simple, enabling miniature robots without conventional link mechanisms to be designed. Serially connected FMAs act as a miniature robot manipulator. The kinematics and control algorithm for this type of robot are presented. FMAs combined in parallel act as a multifingered robot hand, with each FMA representing a finger. An algorithm for the cooperative control for such FMAs, the stable region for holding, and its performance have been developed.<<ETX>>
Design, fabrication and control of soft robots
(2015)

Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.
Monolithic fabrication of sensors and actuators in a soft robotic gripper
(2015)

In this paper, we present a fluidically functionalized soft-bodied robot that integrates both sensing and actuation. Rather than combining these functions as an afterthought, we design sensors and actuators into the robot at the onset, both reducing fabrication complexity and optimizing component interactions. We utilize liquid metal strain sensors and pneumatic actuators embedded into a silicone robotic gripper. The robot's body is formed by curing the silicone in complex 3D printed molds. We show that the liquid metal strain gauges provide a repeatable resistance response during robotic actuation. We further show that, given sufficient control over other time-dependent variables, it is possible to determine when the robot begins gripping an object during actuation.
Soft robotic glove for combined assistance and at-home rehabilitation
(2015)

This paper presents a portable, assistive, soft robotic glove designed to augment hand rehabilitation for individuals with functional grasp pathologies. The robotic glove utilizes soft actuators consisting of molded elastomeric chambers with fiber reinforcements that induce specific bending, twisting and extending trajectories under fluid pressurization. These soft actuators were mechanically programmed tomatch and support the range of motion of individual fingers. They demonstrated the ability to generate significant force when pressurized and exhibited low impedance when un-actuated. To operate the soft robotic glove, a control hardware system was designed and included fluidic pressure sensors in line with the hydraulic actuators and a closed-loop controller to regulate the pressure. Demonstrations with the complete system were performed to evaluate the ability of the soft robotic glove to carry out gross and precise functional grasping. Compared to existing devices, the soft robotic glove has the potential to increase user freedom and independence through its portable waist belt pack and open palm design. © 2014 Elsevier B.V. All rights reserved.
Peristaltic locomotion with antagonistic actuators in soft robotics
(2010)

This paper presents a soft robotic platform that exhibits peristaltic locomotion. The design principle is based on the unique antagonistic arrangement of radial/circular and longitudinal muscle groups of Oligochaeta. Sequential antagonistic motion is achieved in a flexible braided mesh-tube structure with NiTi coil actuators. A numerical model for the mesh structure describes how peristaltic motion induces robust locomotion and details the deformation by the contraction of NiTi actuators. Several peristaltic locomotion modes are modeled, tested, and compared on the basis of locomotion speed. The entire mechanical structure is made of flexible mesh materials and can withstand significant external impacts during locomotion. This approach can enable a completely soft robotic platform by employing a flexible control unit and energy sources.
A Fast Distributed Algorithm for Mining Association Rules
(1996)

With the existence of many large transaction databases, the huge amounts of data, the high scalability of distributed systems, and the easy partition and distribution of a centralized database, it is important to inuestzgate eficient methods for distributed mining of association rules. This study discloses some interesting relationships between locally large and globally large itemsets and proposes an interesting distributed association rule mining algorithm, FDM (Fast Distributed Mining of association rules), which generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules. Our performance study shows that FDM has a superior performance over the direct application of a typical sequential algorithm. Further performance enhancement leads to a few variations of the algorithm.
Mathematical methods in solutions of the problems from the Third International Students' Olympiad in Cryptography
(2017)

The mathematical problems and their solutions of the Third International Students’ Olympiad in Cryptography NSUCRYPTO’2016 are presented. We consider mathematical problems related to the construction of algebraic immune vectorial Boolean functions and big Fermat numbers, problems about secrete sharing schemes and pseudorandom binary sequences, biometric cryptosystems and the blockchain technology, etc. Two open problems in mathematical cryptography are also discussed and a solution for one of them proposed by a participant during the Olympiad is described. It was the first time in the Olympiad history.
TraceMixer: Privacy-preserving crowd-sensing sans trusted third party
(2017)

Crowd-sensing promises cheap and easy large scale data collection by tapping into the sensing and processing capabilities of smart phone users. However, the vast amount of fine-grained location data collected raises serious privacy concerns among potential contributors. In this paper, we argue that crowd-sensing has unique requirements w.r.t. privacy and data utility which renders existing protection mechanisms infeasible. We hence propose TraceMixer, a novel location privacy protection mechanism tailored to the special requirements in crowd-sensing. TraceMixer builds upon the well-studied concept of mix zones to provide trajectory privacy while achieving high spatial accuracy. First in this line of research, TraceMixer applies secure two-party computation technologies to realize a trustless architecture that does not require participants to share locations with anyone in clear. We evaluate TraceMixer on real-world datasets to show the feasibility of our approach in terms of privacy, utility, and performance. Finally, we demonstrate the applicability of TraceMixer in a real-world crowd-sensing campaign.
EXPOSURE: Finding Malicious Domains Using Passive DNS Analysis
(2011)

The domain name service (DNS) plays an important role in the operation of the Internet, providing a two-way mapping between domain names and their numerical identifiers. Given its fundamental role, it is not surprising that a wide variety of malicious activities involve the domain name service in one way or another. For example, bots resolve DNS names to locate their command and control servers, and spam mails contain URLs that link to domains that resolve to scam servers. Thus, it seems beneficial to monitor the use of the DNS system for signs that indicate that a certain name is used as part of a malicious operation. In this paper, we introduce EXPOSURE, a system that employs large-scale, passive DNS analysis techniques to detect domains that are involved in malicious activity. We use 15 features that we extract from the DNS traffic that allow us to characterize different properties of DNS names and the ways that they are queried. Our experiments with a large, real-world data set consisting of 100 billion DNS requests, and a real-life deployment for two weeks in an ISP show that our approach is scalable and that we are able to automatically identify unknown malicious domains that are misused in a variety of malicious activity (such as for botnet command and control, spamming, and phishing).
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
(2018)

Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems — the models (often deep networks or wide networks or both) are compute and memory intensive. Low-precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footprint of these deployed models. In this paper, we study the combination of these two techniques and show that the performance of low-precision networks can be significantly improved by using knowledge distillation techniques. Our approach, Apprentice, achieves state-of-the-art accuracies using ternary precision and 4-bit precision for variants of ResNet architecture on ImageNet dataset. We present three schemes using which one can apply knowledge distillation techniques to various stages of the train-and-deploy pipeline.
08 67 0 v 1 [ cs . C V ] 2 3 M ar 2 01 8 Object Detection for Comics using Manga 109 Annotations
(2018)

With the growth of digitized comics, image understanding techniques are becoming important. In this paper, we focus on object detection, which is a fundamental task of image understanding. Although convolutional neural networks (CNN)-based methods archived good performance in object detection for naturalistic images, there are two problems in applying these methods to the comic object detection task. First, there is no large-scale annotated comics dataset. The CNN-based methods require large-scale annotations for training. Secondly, the objects in comics are highly overlapped compared to naturalistic images. This overlap causes the assignment problem in the existing CNN-based methods. To solve these problems, we proposed a new annotation dataset and a new CNN model. We annotated an existing image dataset of comics and created the largest annotation dataset, named Manga109-annotations. For the assignment problem, we proposed a new CNN-based detector, SSD300-fork. We compared SSD300-fork with other detection methods using Manga109-annotations and confirmed that our model outperformed them based on the mAP score.
Simulation of phasor measurement unit (PMU) in MATLAB
(2015)

The advent of phasor measurement unit in power system is revolutionizing the conventional grid towards smart grid. Phasor measurement units are extremely expensive devices which takes into account many aspects of the power system that are not disclosed by the manufacturer while estimating phasors of currents and voltage. This paper aims to build a laboratory prototype of PMU which can estimate the phasor updating process of a commercial PMU at the benefit of improved measurement accuracy, reduced manufacturing cost and increased timely information.
Discriminative Information Retrieval for Knowledge Discovery
(2016)

We propose a framework for discriminative Information Retrieval (IR) atop linguistic features, trained to improve the recall of tasks such as answer candidate passage retrieval, the initial step in text-based Question Answering (QA). We formalize this as an instance of linear feature-based IR (Metzler and Croft, 2007), illustrating how a variety of knowledge discovery tasks are captured under this approach, leading to a 44% improvement in recall for candidate
The Business Value of Business inTelligence
(2004)

This paper focuses on the influence of BI on manufacturing and distribution organizations, although much of what’s covered in it can apply to businesses in non-related industries, as well. To kick it off, the chart below illustrates the primary value propositions of business intelligence and the types of business analytics (e.g., Sales, Inventory, Supplier Performance analysis) that support each value area. The effect of BI on each of these business value areas are then discussed in more detail in the following sections.
Software project management with GAs
(2007)

A Project Scheduling Problem consists in deciding who does what during the software project lifetime. This is a capital issue in the practice of software engineering, since the total budget and human resources involved must be managed optimally in order to end in a successful project. In short, companies are principally concerned with reducing the duration and cost of projects, and these two goals are in conflict with each other. In this work we tackle the problem by using genetic algorithms (GAs) to solve many different software project scenarios. Thanks to our newly developed instance generator we can perform structured studies on the influence the most important problem attributes have on the solutions. Our conclusions show that GAs are quite flexible and accurate for this application, and an important tool for automatic project management. 2007 Elsevier Inc. All rights reserved.
Language processing and learning models for community question answering in Arabic
(2017)

In this paper we focus on the problem of question ranking in community question answering (cQA) forums in Arabic. We address the task with machine learning algorithms using advanced Arabic text representations. The latter are obtained by applying tree kernels to constituency parse trees combined with textual similarities, including word embeddings. Our two main contributions are: (i) an Arabic language processing pipeline based on UIMA —from segmentation to constituency parsing— built on top of Farasa, a state-of-the-art Arabic language processing toolkit; and (ii) the application of long short-term memory neural networks to identify the best text fragments in questions to be used in our tree-kernel-based ranker. Our thorough experimentation on a recently released cQA dataset shows that the Arabic linguistic processing provided by Farasa produces strong results and that neural networks combined with tree kernels further boost the performance in terms of both efficiency and accuracy. Our approach also enables an implicit comparison between different processing pipelines as our tests on Farasa and Stanford parsers demonstrate.
Simulating LTE/LTE-Advanced Networks with SimuLTE
(2014)

In this work we present SimuLTE, an OMNeT++-based simulator for LTE and LTE-Advanced networks. Following well-established OMNeT++ programming practices, SimuLTE exhibits a fully modular structure, which makes it easy to be extended, verified and integrated. Moreover, it inherits all the benefits of such a widely-used and versatile simulation framework as OMNeT++, i.e., experiment support and seamless integration with the OMNeT++ network modules, such as INET. This allows SimuLTE users to build up mixed scenarios where LTE is only a part of a wider network. This paper describes the architecture of SimuLTE, with particular emphasis on the modeling choices at the MAC layer, where resource scheduling is located. Furthermore, we describe some of the verification and validation efforts and present an example of the performance analysis that can be carried out with SimuLTE.